Breaking Cycles In Noisy Hierarchies
Jiankai Sun, Deepak Ajwani, Patrick K. Nicholson, Alessandra Sala, Srinivasan Parthasarathy · 2017
Taxonomy graphs that capture hyponymy or meronymy relationships through directed edges are expected to be acyclic. However, in practice, they may have thousands of cycles, as they are often created in a crowd-sourced way. Since these cycles represent logical fallacies, they need to be removed for many web applications. In this paper, we address the problem of breaking cycles while preserving the logical structure (hierarchy) of a directed graph as much as possible. Existing approaches for this problem either need manual intervention or use heuristics that can critically alter the taxonomy structure. In contrast, our approach infers graph hierarchy using a range of features, including a Bayesian skill rating system and a social agony metric. We also devise several strategies to leverage the inferred hierarchy for removing a small subset of edges to make the graph acyclic. Extensive experiments demonstrate the effectiveness of our approach.